CN116709240A - Hierarchical sensor deployment method based on whale optimization algorithm - Google Patents
Hierarchical sensor deployment method based on whale optimization algorithm Download PDFInfo
- Publication number
- CN116709240A CN116709240A CN202310756910.1A CN202310756910A CN116709240A CN 116709240 A CN116709240 A CN 116709240A CN 202310756910 A CN202310756910 A CN 202310756910A CN 116709240 A CN116709240 A CN 116709240A
- Authority
- CN
- China
- Prior art keywords
- sensor
- area
- sensors
- battlefield
- detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 241000283153 Cetacea Species 0.000 title claims abstract description 36
- 238000005457 optimization Methods 0.000 title claims abstract description 32
- 230000003068 static effect Effects 0.000 claims abstract description 17
- 239000011159 matrix material Substances 0.000 claims abstract description 10
- 238000001514 detection method Methods 0.000 claims description 50
- 230000008859 change Effects 0.000 claims description 11
- 230000036039 immunity Effects 0.000 claims 1
- 230000006870 function Effects 0.000 description 16
- 230000008569 process Effects 0.000 description 5
- 238000004088 simulation Methods 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The application provides a layering sensor deployment method based on a whale optimization algorithm, which comprises the steps of initializing parameters of a sensor group, initializing the number of sensors, and initializing a battlefield network model and a battlefield layout constraint matrix according to battlefield environment information; optimizing and deploying the static sensors in the sensor group by taking the maximized effective coverage rate as an objective function, and iterating to obtain the optimized static sensor positions according to the steps of a whale optimizing algorithm; optimizing and distributing the dynamic sensors in the sensor group with a finer objective function according to the environment and situation of the current battlefield; the sensor network in the method provided by the application consists of a static sensor and a dynamic sensor. The static sensor is higher in level, all dynamic sensor positions of the detected area can be obtained, and the dynamic sensor has the moving capability and can accurately move to the optimized position. The method can still accurately acquire the target information of the high maneuvering target under the complex battlefield environment.
Description
Technical Field
The application belongs to the field of sensor network deployment, and particularly relates to a layering sensor deployment method based on a whale optimization algorithm.
Background
In a networked collaborative fire control system, the task of a scout sensing network is to collect and preprocess various information of a battlefield space and fuse the obtained information into a consistent battlefield situation. At present, related researches on a sensor distribution method mostly adopt artificial intelligent optimization algorithms, such as genetic algorithms, ant colony algorithms and the like, which are proposed by maximizing effective coverage rate and efficiency factors of sensor targets as objective functions, and the algorithms are all based on the efficiency maximization to realize intelligent distribution of the sensors.
When a high maneuvering target exists in a battlefield, the battlefield situation changes quickly, and the objective function value also changes greatly. To accommodate such a rapidly changing environment, the sensor position must be constantly optimized. Based on different characteristics and purposes of various sensors, the positions of all sensors are continuously optimized, the efficiency is low, and unnecessary resource waste can be caused.
Disclosure of Invention
The application aims to solve the technical problems of low sensor deployment optimization efficiency and resource waste caused by continuously changing battlefield situations.
The technical scheme of the application is that the layering sensor deployment method based on the whale optimization algorithm comprises the following steps:
step 1, initializing parameters of a sensor group, the number of the sensors, and initializing a battlefield network model and a battlefield layout constraint matrix according to battlefield environment information.
And 2, optimizing and deploying the static sensors in the sensor group by taking the maximized effective coverage rate as an objective function, and iterating to obtain the optimized static sensor positions according to the step of a whale optimizing algorithm.
And 3, optimizing and distributing the dynamic sensors in the sensor group according to the environment and situation of the current battlefield by using a finer objective function.
And step 4, detecting whether the battlefield environment and situation change, judging whether the deployment of the current sensor network needs to be optimized, and if so, performing step 3.
Optionally, the battlefield network model is determined by the following method:
assuming that the whole sensor detection area is a two-dimensional plane and is digitized into an L×M grid;
the sensor can be deployed in any point in the grid, and the whole detection area is divided into squares;
the cell covered by each sensor is determined from the coverage of the sensor.
3. The method of claim 1, wherein the area incapable of being sited is defined as a in a battlefield sited constraint matrix unable The area where stations can be located is defined as A able The battlefield geographic constraint is expressed as:
b ij ∈A able ≠A unable 。
optionally, the indexes met by the objective function include core area coverage, anti-stealth capability, co-channel interference avoidance, anti-interference capability and resource utilization capability.
Optionally, the index of the objective function is defined as:
(1) Core area coverage: the core area coverage coefficient beta is defined as the actually obtained core detection area and core area a c Wherein N represents the total number of sensors, A i And A is a c The intersection represents the core detection area actually obtained by the ith sensor:
(2) Anti-stealth capability: the anti-stealth capability coefficient alpha is defined as a responsibility detection area and a responsibility area A actually obtained by a reconnaissance system r Wherein N represents the total number of sensors, A i And A is a r The intersection represents the actual acquired area of responsibility detection that the ith sensor actually acquired:
(3) Avoiding co-channel interference: in order to avoid the influence of frequency interference on the detection performance of the system, the sensors with overlapped frequencies cannot be too close to each other and the co-channel interference is avoided, so that the co-channel interference coefficient theta is defined as the degree of co-channel interference between the adjacent sensors i and j, wherein N represents the total number of the sensors, and f i Representing the frequency range of sensor i, f j Representing the frequency range of sensor j, f i Intersection f j Representing the frequency of overlap between the two sensors:
(4) Interference resistance: the anti-interference capacity coefficient mu is defined as the proportion of the effective responsibility area covered by N radars to the total responsibility area, wherein S is a given responsibility area and S i The detection area of the stealth target is the i-th radar.
(5) Resource utilization capability: the resource utilization coefficient eta is defined as the coverage area and the core area A of the sensor below 3 parts c Ratio of (3):
in order to meet the 5 principles, comprehensively considering battlefield geographic constraint and tactical condition limitation, a weighting method is used for establishing a multi-sensor optimized deployment model as follows:
optionally, optimizing the deployment of the dynamic sensors in the sensor group with a finer objective function according to the environment and situation of the current battlefield includes:
by [ (x) 1 ，y 1 )，(x 2 ，y 2 )，...，(x n ，y n )]Represents whale individuals, wherein (x i ，y i ) The coordinate position of the ith sensor is the coordinate position of the ith sensor, the sensors are positioned in the grid, and n is the number of the sensors;
deploying the sensor according to situation change (for example, a certain area has an important target and needs important reconnaissance, or the environment of a certain area has change, and the sensor cannot be deployed or needs evacuation); the situation change in the battlefield comprises the occurrence of an important target in a target area, and the important reconnaissance is carried out; or the environment of the target area changes, and the sensor cannot be deployed or needs to be evacuated;
the whale optimizing algorithm comprises the following steps:
step 31, initializing parameters of a sensor group, the number of sensors, the maximum detection radius of each sensor, the detection power and the detection probability of each sensor, the area of an area to be detected in a battlefield and a battlefield layout constraint matrix;
step 32, initializing parameters of a whale optimization algorithm, the number of whales and the maximum iteration number;
step 33, calculating population fitness according to the objective function, and finding out the current global optimal solution position of the population;
step 34, judging whether the random number P is smaller than 0.5, if yes, executing step 35; otherwise, the spiral position update is performed according to the following method:
step 35, updating the parameter A, if |A| <1, updating the whale position according to the formula (1), otherwise updating according to the formula (2).
Step 36, judging whether the algorithm reaches the maximum iteration times, and if so, outputting a global optimal solution, namely, optimizing each parameter and corresponding sensor deployment position coordinates; otherwise, step 33 is performed for the next iteration.
The application has the following technical advantages
(1) Compared with a sensor optimal deployment method for uniformly and optimally deploying all sensors, the application provides a layering sensor deployment method based on a whale optimization algorithm, wherein the sensors are classified into two types of static sensors and dynamic sensors, the static sensors are higher in level and can control the dynamic sensors in a range, and a more reasonable sensor deployment strategy can be provided. The method provided by the application can save resources and provide faster optimized deployment speed.
(2) The layering sensor deployment method based on the whale optimization algorithm provided by the application uses the whale optimization algorithm when optimizing the sensor position. Through experimental tests, compared with the common optimization algorithm, the optimization algorithm has higher iteration speed while providing better optimization precision.
Drawings
FIG. 1 is a flowchart of a layering sensor deployment method based on whale optimization algorithm.
FIG. 2 is a sensor optimization deployment iteration process of the PSO algorithm in an embodiment of the present application.
FIG. 3 is a sensor optimization deployment iteration process of the GA algorithm in an embodiment of the present application.
FIG. 4 is a sensor optimization deployment iteration process of the GA-PSO algorithm in an embodiment of the present application.
FIG. 5 is a sensor optimization deployment iteration process of the WOA algorithm in an embodiment of the application.
Detailed Description
Referring to fig. 1, the layering sensor deployment method based on whale optimization algorithm of the application comprises the following steps:
step 1, initializing parameters of a sensor group, the number of sensors, the maximum detection radius of each sensor, the detection power and the detection probability of each sensor, and initializing a battlefield network model and a battlefield layout constraint matrix according to battlefield environment information.
And 2, optimizing and deploying the static sensors in the sensor group by taking the maximized effective coverage rate as an objective function, and iterating to obtain the optimized static sensor positions according to the step of a whale optimizing algorithm.
And 3, optimizing and distributing the dynamic sensors in the sensor group according to the environment and situation of the current battlefield by using a finer objective function.
And step 4, detecting whether the battlefield environment and situation change, judging whether the deployment of the current sensor network needs to be optimized, and if so, performing step 3.
Further, the battlefield sensor deployment model in step 1 specifically includes: assuming that the whole detection area of the sensor is a two-dimensional plane and is digitized into an L×M grid, the sensor can be deployed in any point in the grid, the whole detection area is divided into squares, and the unit cell covered by each sensor can be calculated according to the coverage area of the sensor.
Assume that the sensors have different perceived radii r s And an indeterminate perception range delta r s (delta < 1), grid center point coordinates T (x i ，y i ) And a certain sensor S j (x sj ，y sj ) The distance between them isIn the binary sensor coverage model, the sensing probability of the sensor node to the grid area T is:
where λ is the sensor sensing capability index. When the sensing probability of the grid area is larger than a certain threshold value, the grid can be sensed by the sensor node, and the area is an effective coverage point; otherwise, the area is considered to be not perceivable by the sensor node. The sensing probability of a single sensor on a certain area is generally smaller than 1, which means that a plurality of sensors need to be adopted for simultaneous detection in the detection process so as to improve the target sensing probability.
In the grid, an area where a station cannot be located is defined as A unable The area where stations can be located is defined as A able Then the battlefield geographic constraint can be expressed as
b ij ∈A able ≠A unable
In addition, in order to realize the functions of information interaction, collaborative detection and mutual blind compensation between the sensors, a certain distance limit is needed between any two sensors, and the sensors cannot be too large or too small. The distance between two adjacent sensors may be constrained by:
|r i -r j |≤d(S i ，S j )≤r i +r j
further, the objective function in step 2 and step 3 is defined as:
in order to realize tactical requirements of anti-interference, anti-stealth and the like of the sensor network, the following principles should be followed when the sensor is deployed:
(1) Core area coverage: in practical application, some areas are important scout areas and should be covered, so the core area coverage coefficient β is defined as the actually obtained core detection area and core area a c Wherein N represents the total number of sensors, A i And A is a c The intersection represents the core detection area actually obtained by the ith sensor:
(2) Anti-stealth capability: based on the existing radar resources, the detection range of a typical stealth target is as large as possible, and the anti-stealth capability coefficient alpha is defined as the actual practice of a scout systemAcquired responsibility detection area and responsibility area A r Wherein N represents the total number of sensors, A i And A is a r The intersection represents the actual acquired area of responsibility detection that the ith sensor actually acquired:
(3) Avoiding co-channel interference: in order to avoid the influence of frequency interference on the detection performance of the system, the sensors with overlapped frequencies cannot be too close to each other and the co-channel interference is avoided, so that the co-channel interference coefficient theta is defined as the degree of co-channel interference between the adjacent sensors i and j, wherein N represents the total number of the sensors, and f i Representing the frequency range of sensor i, f j Representing the frequency range of sensor j, f i Intersection f j Representing the frequency of overlap between the two sensors:
(4) Interference resistance: the proper airspace coverage redundancy can have stronger survivability when suffering from the interference of the environment or enemy, but excessive redundancy can cause the waste of resources, and the anti-interference capability coefficient mu is defined as the proportion of the effective responsibility area covered by N radars to the total responsibility area, wherein S is a given responsibility area, S i The detection area of the stealth target is the i-th radar.
(5) Resource utilization capability: normally, overlapping of 2 sensor coverage areas is considered reasonable, overlapping of 3 or more sensor coverage areas is considered wasteful, and therefore, the resource utilization coefficient η is defined as the coverage area and core area a of the 3 or less sensors c Ratio of (3):
in order to meet the 5 principles, comprehensively considering battlefield geographic constraint and tactical condition limitation, a weighting method is used for establishing a multi-sensor optimized deployment model as follows:
step 3, in the whale optimization algorithm distributed towards the sensor, using [ (x) 1 ,y 1 ),(x 2 ,y 2 ),，(x n ，y n )]Represents whale individuals, wherein (x i ，y i ) For the coordinate position of the ith sensor, the sensors are located in a grid, and n is the number of sensors. The sensor is deployed according to situation changes in the battlefield (for example, a certain area has an important target, important reconnaissance is needed, or the environment of a certain area has a change, and the sensor cannot be deployed or needs to be evacuated). The following are the specific algorithm steps of the whale optimization algorithm for sensor-oriented distribution:
and step 31, initializing parameters of a sensor group, the number of sensors, the maximum detection radius of each sensor, the detection power and the detection probability of each sensor, the area of an area to be detected in a battlefield and a battlefield layout constraint matrix.
And step 32, initializing parameters of a whale optimization algorithm, the number of whales and the maximum iteration number.
And step 33, calculating the population fitness according to the objective function, and finding out the current global optimal solution position of the population.
Step 34, judging whether the random number P is smaller than 0.5, if yes, executing step 35; otherwise, the spiral position update is performed according to the following formula.
Step 35, updating the parameter A, if |A| <1, updating the whale position according to formula (1), otherwise updating according to formula (2).
Step 36, judging whether the algorithm reaches the maximum iteration times, and if so, outputting a global optimal solution, namely, optimizing each parameter and corresponding sensor deployment position coordinates; otherwise, step 33 is performed for the next iteration.
Examples
With reference to fig. 1, the hierarchical sensor deployment method for high maneuvering target detection in a complex land battlefield of the present application comprises the following steps:
(1) Parameters of the sensor group are initialized, including the number of sensors, the maximum detection radius of each sensor, the detection power of each sensor, the detection probability, the area of an area to be detected in a battlefield and a battlefield layout constraint matrix.
(2) Optimizing and deploying the static sensors in the sensor group by taking the maximum effective coverage rate as an objective function, and iterating to obtain the optimized static sensor positions according to the steps of the whale optimizing algorithm.
(3) The dynamic sensors in the sensor group are optimally distributed according to the environment and situation of the current battlefield with finer objective functions.
(4) Detecting whether the battlefield environment and situation change, judging whether the deployment of the current sensor network needs to be optimized, and if so, performing step 3.
In this embodiment, the reconnaissance area of the target setting battlefield is 100km×100km, and the core area is 20km×20km. The number of the sensors is limited, 2 static sensors are adopted for station arrangement, the maximum detection radius (km) of the sensors is respectively set to be 20km and 25km, the detection probability is 0.8, and the weight coefficients of the objective functions are set to be 1,0 and 0. The total of 8 parts of the dynamic sensor is provided with maximum detection radiuses (km) of 5km, 4km, 3km and 3.5km, detection frequencies of [5,7.5], [0.3,0.6], [7,8], [0.5,1.5] and detection probability of 0.8.
The number of the whale population is initialized to be 50, the maximum iteration number is 200, in the embodiment, the PSO, GA, GA-PSO algorithm and the WOA algorithm are respectively adopted to perform 10 rounds of simulation and average, simulation results are shown in a table 1, and the WOA algorithm has the highest iteration speed and better coverage rate when having the same PSO algorithm and the GA-PSO algorithm close to the optimizing precision.
TABLE 1 simulation results
The station situation diagrams drawn based on the optimal values are shown in fig. 2 to 5. The optimal positions of the 4 sensors obtained by the station distribution optimization based on the WOA algorithm are obviously improved compared with the station distribution positions obtained by other 3 algorithms, so that the sensors are better prevented from being arranged in the area where the station cannot be distributed, and the station distribution principle is better followed. When the situation changes, the key area needs to be covered completely, and the coverage rate of the key area can almost reach one hundred percent; when interference occurs, the re-station will make appropriate redundancy of detection ranges for the interfering region.
The application has the following technical advantages
(1) Compared with a sensor optimal deployment method for uniformly and optimally deploying all sensors, the application provides a layering sensor deployment method based on a whale optimization algorithm, wherein the sensors are classified into two types of static sensors and dynamic sensors, the static sensors are higher in level and can control the dynamic sensors in a range, and a more reasonable sensor deployment strategy can be provided. The method provided by the application can save resources and provide faster optimized deployment speed.
(2) The layering sensor deployment method based on the whale optimization algorithm provided by the application uses the whale optimization algorithm when optimizing the sensor position. Through experimental tests, compared with the common optimization algorithm, the optimization algorithm has higher iteration speed while providing better optimization precision.
Claims (6)
1. The layering sensor deployment method based on the whale optimization algorithm is characterized by comprising the following steps of:
step 1, initializing parameters of a sensor group, the number of the sensors, and initializing a battlefield network model and a battlefield layout constraint matrix according to battlefield environment information;
step 2, optimizing and deploying the static sensors in the sensor group by taking the maximized effective coverage rate as an objective function, and iterating to obtain the optimized static sensor positions according to the step of whale optimizing algorithm;
step 3, optimizing and distributing the dynamic sensors in the sensor group according to the environment and situation of the current battlefield by using a finer objective function;
and step 4, detecting whether the battlefield environment and situation change, judging whether the deployment of the current sensor network needs to be optimized, and if so, executing the step 3.
2. The method of claim 1, wherein the battlefield network model is determined by:
assuming that the whole sensor detection area is a two-dimensional plane and is digitized into an L×M grid;
the sensor can be deployed in any point in the grid, and the whole detection area is divided into squares;
the cell covered by each sensor is determined from the coverage of the sensor.
3. The method of claim 1, wherein the area incapable of being sited is defined as a in a battlefield sited constraint matrix unable The area where stations can be located is defined as A able The battlefield geographic constraint is expressed as:
b ij ∈A able ≠A unable 。
4. the method of claim 1, wherein the metrics satisfied by the objective function include core area coverage, anti-stealth capability, co-channel interference avoidance, interference immunity, resource utilization capability.
5. The method of claim 4, wherein the target function is defined as:
(1) Core area coverage: the core area coverage coefficient beta is defined as the actually obtained core detection area and core area a c Wherein N represents the total number of sensors, A i And A is a c The intersection represents the core detection area actually obtained by the ith sensor:
(2) Anti-stealth capability: the anti-stealth capability coefficient alpha is defined as a responsibility detection area and a responsibility area A actually obtained by a reconnaissance system r Wherein N represents the total number of sensors, A i And A is a r The intersection represents the actual acquired area of responsibility detection that the ith sensor actually acquired:
(3) Avoiding co-channel interference: in order to avoid the influence of frequency interference on the detection performance of the system, the sensors with overlapped frequencies cannot be too close to each other and the co-channel interference is avoided, so that the co-channel interference coefficient theta is defined as the degree of co-channel interference between the adjacent sensors i and j, wherein N represents the total number of the sensors, and f i Representing the frequency range of sensor i, f j Representing the frequency range of sensor j, f i Intersection f j Representing the frequency of overlap between the two sensors:
(4) Interference resistance: the anti-interference capacity coefficient mu is defined as the proportion of the effective responsibility area covered by N radars to the total responsibility area, wherein S is a given responsibility area and S i The detection area of the stealth target is the i-th radar.
(5) Resource utilization capability: the resource utilization coefficient eta is defined as the coverage area and the core area A of the sensor below 3 parts c Ratio of (3):
in order to meet the 5 principles, comprehensively considering battlefield geographic constraint and tactical condition limitation, a weighting method is used for establishing a multi-sensor optimized deployment model as follows:
6. the method of claim 1, wherein optimizing the deployment of the dynamic sensors in the sensor group with a finer objective function based on the environment and situation of the current battlefield, comprises:
by [ (x) 1 ，y 1 )，(x 2 ，y 2 )，...，(x n ，y n )]Represents whale individuals, wherein (x i ，y i ) The coordinate position of the ith sensor is the coordinate position of the ith sensor, the sensors are positioned in the grid, and n is the number of the sensors;
deploying the sensor according to situation change (for example, a certain area has an important target and needs important reconnaissance, or the environment of a certain area has change, and the sensor cannot be deployed or needs evacuation); the situation change in the battlefield comprises the occurrence of an important target in a target area, and the important reconnaissance is carried out; or the environment of the target area changes, and the sensor cannot be deployed or needs to be evacuated;
the whale optimizing algorithm comprises the following steps:
step 31, initializing parameters of a sensor group, the number of sensors, the maximum detection radius of each sensor, the detection power and the detection probability of each sensor, the area of an area to be detected in a battlefield and a battlefield layout constraint matrix;
step 32, initializing parameters of a whale optimization algorithm, the number of whales and the maximum iteration number;
step 33, calculating population fitness according to the objective function, and finding out the current global optimal solution position of the population;
step 34, judging whether the random number P is smaller than 0.5, if yes, executing step 35; otherwise, the spiral position update is performed according to the following method:
step 35, updating the parameter A, if |A| <1, updating the whale position according to the formula (1), otherwise updating according to the formula (2).
Step 36, judging whether the algorithm reaches the maximum iteration times, and if so, outputting a global optimal solution, namely, optimizing each parameter and corresponding sensor deployment position coordinates; otherwise, step 33 is performed for the next iteration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310756910.1A CN116709240B (en) | 2023-06-26 | 2023-06-26 | Hierarchical sensor deployment method based on whale optimization algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310756910.1A CN116709240B (en) | 2023-06-26 | 2023-06-26 | Hierarchical sensor deployment method based on whale optimization algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116709240A true CN116709240A (en) | 2023-09-05 |
| CN116709240B CN116709240B (en) | 2024-03-12 |
Family
ID=87825520
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310756910.1A Active CN116709240B (en) | 2023-06-26 | 2023-06-26 | Hierarchical sensor deployment method based on whale optimization algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116709240B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572295A (en) * | 2024-01-12 | 2024-02-20 | 山东和兑智能科技有限公司 | Multi-mode on-line monitoring and early warning method for high-voltage sleeve |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130218499A1 (en) * | 2010-07-27 | 2013-08-22 | Thales | Method for Optimally Determining the Characteristics and Arrangement of a Set of Sensors for Monitoring an Area |
| CN110996287A (en) * | 2019-12-04 | 2020-04-10 | 上海工程技术大学 | Network node selection method, system and storage medium based on whale optimization algorithm |
| CN110996333A (en) * | 2019-11-06 | 2020-04-10 | 湖北工业大学 | Wireless sensor network node positioning method based on whale algorithm |
| CN114827929A (en) * | 2022-03-16 | 2022-07-29 | 西安电子科技大学 | Multi-sensor network collaborative deployment method and device based on improved particle swarm optimization |
| CN115866621A (en) * | 2022-11-08 | 2023-03-28 | 重庆邮电大学 | Wireless sensor network coverage method based on whale algorithm |
| CN116070672A (en) * | 2023-01-18 | 2023-05-05 | 福州大学 | Optimization problem solving method based on improved whale optimization algorithm |
-
2023
- 2023-06-26 CN CN202310756910.1A patent/CN116709240B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130218499A1 (en) * | 2010-07-27 | 2013-08-22 | Thales | Method for Optimally Determining the Characteristics and Arrangement of a Set of Sensors for Monitoring an Area |
| CN110996333A (en) * | 2019-11-06 | 2020-04-10 | 湖北工业大学 | Wireless sensor network node positioning method based on whale algorithm |
| CN110996287A (en) * | 2019-12-04 | 2020-04-10 | 上海工程技术大学 | Network node selection method, system and storage medium based on whale optimization algorithm |
| CN114827929A (en) * | 2022-03-16 | 2022-07-29 | 西安电子科技大学 | Multi-sensor network collaborative deployment method and device based on improved particle swarm optimization |
| CN115866621A (en) * | 2022-11-08 | 2023-03-28 | 重庆邮电大学 | Wireless sensor network coverage method based on whale algorithm |
| CN116070672A (en) * | 2023-01-18 | 2023-05-05 | 福州大学 | Optimization problem solving method based on improved whale optimization algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 俞宙;单甘霖;段修生;: "面向区域覆盖的多传感器优化布站", 现代防御技术, no. 06, pages 98 - 105 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572295A (en) * | 2024-01-12 | 2024-02-20 | 山东和兑智能科技有限公司 | Multi-mode on-line monitoring and early warning method for high-voltage sleeve |
| CN117572295B (en) * | 2024-01-12 | 2024-04-12 | 山东和兑智能科技有限公司 | Multi-mode on-line monitoring and early warning method for high-voltage sleeve |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116709240B (en) | 2024-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108616302B (en) | Unmanned aerial vehicle multiple coverage model under power control and deployment method | |
| CN103476118B (en) | A kind of WLAN indoor location fingerprint positioning method for monitoring in real time | |
| CN116709240B (en) | Hierarchical sensor deployment method based on whale optimization algorithm | |
| CN111294921B (en) | RSSI wireless sensor network three-dimensional cooperative positioning method | |
| CN107277830A (en) | A kind of wireless sensor network node dispositions method based on particle group optimizing and mutation operator | |
| CN107113764A (en) | The method and apparatus for improving artificial neural network positioning performance | |
| CN113573322B (en) | Multi-target area sensor network coverage optimization method based on improved genetic algorithm | |
| CN106792764B (en) | Wireless base station planning method for power distribution terminal communication access network | |
| CN110765586B (en) | Radar networking optimization station arrangement method based on improved particle swarm algorithm | |
| CN101893441A (en) | Unmanned aerial vehicle flight path optimization method based on deviation maximization and grey correlation analysis | |
| CN115632956A (en) | Chargeable Internet of things node optimization deployment method based on trusted information coverage | |
| Chiroma et al. | Large scale survey for radio propagation in developing machine learning model for path losses in communication systems | |
| CN114444398B (en) | Grey wolf algorithm-based networking radar cooperative interference resource allocation method | |
| CN113115342B (en) | WSNs deployment method and system of virtual force-guided sparrow search algorithm | |
| Gupta et al. | Multi-uav deployment for noma-enabled wireless networks based on imogwo algorithm | |
| CN111709584B (en) | Radar networking optimization deployment method based on artificial bee colony algorithm | |
| Campbell et al. | Minimising rf detectability for low probability of detection communication | |
| CN108040338B (en) | deployment method of wireless sensor network in environment with irregular distribution of monitored targets | |
| CN114065518B (en) | Interference resource allocation method and system | |
| CN112434857B (en) | Unmanned aerial vehicle airport network site selection arrangement method based on GA-PSO optimization | |
| Lavanya et al. | Model selection for path loss prediction in wireless networks | |
| Zhao et al. | Fuzzy deployment for wireless sensor networks | |
| CN113590587A (en) | Offline position fingerprint database construction method based on self-adaptive simulated annealing-particle swarm-kriging interpolation algorithm | |
| Deng et al. | Interference resource scheduling algorithm based on potential game for UAV cooperative combat | |
| Pan et al. | Optimal Deployment of Radar Network based on Chaos Genetic Algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |